Tomography is based on utilizing data sets to obtain images or infer the optical properties of the medium under study. Many different numerical and analytical approaches have been developed for modeling photon propagation and effectively provide the solutions necessary for tomographic inversion of the data sets collected. This has grown into an active field (see, e.g., Ntziachristos, Ripoll et al. (2005) “Looking and listening to light: the evolution of whole-body photonic imaging.” Nat. Biotechnol. 23 (3): 313-320 and Arridge, Dehghani et al. (2000) “The finite element model for the propagation of light in scattering media: a direct method for domains with nonscattering regions.” Med. Phys. 27(1):252-264 for reviews). For the most general case of imaging a subject with arbitrary geometries, numerical techniques are most pertinent. In all these numerical methods, the objective is to determine the distribution of luminescent or fluorescent sources inside a heterogeneous or homogeneous medium from a relatively small number of surface measurements (in the order of 102-103). With improvements in detector technology and computational platforms, higher numbers of measurements have become possible, which has led to new numerical methods that can deal with such large data sets. These techniques have been developed so as to reduce the memory and computing time needed to solve for such large systems of equations (see, e.g., Ripoll, Ntziachristos et al. (2001) “The Kirchhoff Approximation for diffusive waves.” Phys. Rev. E. 64: 051917:1-8; Ripoll, Nieto-Vesperinas et al. (2002) “Fast analytical approximation for arbitrary geometries in diffuse optical tomography.” Opt. Let. 27(7): 527-529; and Markel and Schotland (2004) “Symmetries, inversion formulas, and image reconstruction for optical tomography.” Phys. Rev. E Stat Nonlin Soft Matter Phys. 70(5 Pt 2): 056616). At present, however, a point has been reached where a compromise is needed when dealing with arbitrary geometries: either accurate methods are used by reducing considerably the size of the data sets (see, e.g., Hielscher, Alcouffe et al. (1998) “Comparison of finite-difference transport and diffusion calculations for photon migration in homogenous and heterogeneous tissues.” Phys. Med. Biol. 43: 1285-1302 and Arridge, Dehghani et al. (2000) “The finite element model for the propagation of light in scattering media: a direct method for domains with nonscattering regions.” Med. Phys. 27(1):252-264), or approximate methods are employed to speed up calculations at the expense of the accuracy of the result (see, e.g., Ripoll, Ntziachristos et al. (2001) “The Kirchhoff Approximation for diffusive waves.” Phys. Rev. E. 64: 051917:1-8).